Run UMAP dimension reduction — runUMAPprojection • Giotto

run UMAP on subset and project on the rest

runUMAPprojection(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  dim_reduction_to_use = "pca",
  dim_reduction_name = NULL,
  dimensions_to_use = 1:10,
  random_subset = 500,
  name = NULL,
  feats_to_use = NULL,
  return_gobject = TRUE,
  n_neighbors = 40,
  n_components = 2,
  n_epochs = 400,
  min_dist = 0.01,
  n_threads = NA,
  spread = 5,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = TRUE,
  toplevel_params = deprecated(),
  toplevel = 1L,
  ...
)

Arguments

gobject: giotto object
feat_type: feature type
spat_unit: spatial unit
expression_values: expression values to use
reduction: 'cells' or 'feats'
dim_reduction_to_use: use another dimension reduction set as input
dim_reduction_name: name of dimension reduction set to use
dimensions_to_use: number of dimensions to use as input
random_subset: random subset to perform UMAP on
name: arbitrary name for UMAP run
feats_to_use: if dim_reduction_to_use = NULL, which features to use
return_gobject: boolean: return giotto object (default = TRUE)
n_neighbors: UMAP param: number of neighbors
n_components: UMAP param: number of components
n_epochs: UMAP param: number of epochs
min_dist: UMAP param: minimum distance
n_threads: UMAP param: threads/cores to use
spread: UMAP param: spread
set_seed: use of seed
seed_number: seed number to use
verbose: verbosity of function
toplevel_params: deprecated
toplevel: relative stackframe where call was made from
...: additional UMAP parameters

Value

giotto object with updated UMAP dimension reduction

Details

See umap for more information about these and other parameters.

Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')
To use gene expression as input set dim_reduction_to_use = NULL
If dim_reduction_to_use = NULL, feats_to_use can be used to select a column name of highly variable genes (see calculateHVF) or simply provide a vector of genes
multiple UMAP results can be stored by changing the name of the analysis

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

runUMAPprojection(g)
#> Setting dimension reduction [cell][rna] umap.projection
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> dimensions    : 634, 624 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap umap.projection tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used